Development of a Novel and Robust Virtual Flow Meter (VFM) for Geothermal Applications
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Geothermal energy, VFM↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Rancor-HUNTER: A Virtual Plant and Operator Environment for Predicting Human Performance
Advances in simulation capabilities to model physical systems have outpaced the development of simulations for humans using those physical systems. There is an argument that the infinite span of potential human behaviors inherently render human modeling more challenging than physical systems. Despite this challenge, the need for modeling humans interacting with these complex systems is paramount. As technologies have improved, many of the failure modes originating from the physical systems have been solved. This means the overall proportion of human errors has increased, such that it is not uncommon to be the primary driver of system failure in modern complex systems. Moreover, technologies such as automated systems may introduce emerging contexts that can cause new, unanticipated modes of human error. Therefore, it is now more important than ever to develop models of human behavior to realize overall system error reductions and achieve established safety margins. To support new and novel concepts of operations for the anticipated wave of advanced nuclear reactor deployments, human factors and human reliability analysis researchers need to develop advanced simulation-based approaches. This talk presents a simulation environment suitable to both collect data and then perform Monte Carlo simulations to evaluate human performance and develop better models of human behavior. Specifically, the Rancor Microworld Simulator models a complex energy production system in a simplified manner. Rancor includes computer-based procedures, which serve as a framework to automatically classify human behaviors without manual, subjective experimenter coding during scenarios. This method supports a detailed level of analysis at the task level. It is feasible for collecting large sample sizes required to develop quantitative modelling elements that have historically challenged traditional full-scope simulator study approaches. Additionally, the other portion of this experimental platform, the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), is presented to show how the collected data can be used to evaluate novel scenarios based on the contextual factors, or performance shaping factors, derived from Rancor simulations. Rancor-HUNTER is being used to predict operator performance with new procedures, such as results from control room modernization or new-build situations. Rancor-HUNTER is also proving a useful surrogate platform to model human performance for other complex systems.
99 GENERAL AND MISCELLANEOUS↗
Virtual Reliability Software for Real Time Detection of Failures at PV Plants
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14 SOLAR ENERGY↗
Establishing Digital Twins for Non-Proliferation of Secure Facilities under Low Data Availability with Agent-Based Modeling, Deep Neural Network Surrogates, and Virtual Reality
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Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
Analysis of Electromagnetic Calorimeters and Application to the Deeply Virtual Compton Scattering Experiment at Hall C of Jefferson Lab.
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Hamdi, Wassim [Monastir University]↗
Virtual Growth of SRF Materials: A Machine Learning Approach to Predict the Crystalline Structural Ordering in Nb Surface Oxides
Niobium's native surface oxide affects SRF cavity and superconducting qubit performance, motivating interest in controlling its crystalline structure. We combine a literature-derived machine-learning analysis with temperature-dependent XRD to study crystalline ordering in Nb2O5. Random Forest models, trained on 74 processing conditions from 17 papers and validated by leave-one-group-out cross-validation, predicted broad crystallinity outcomes well (balanced accuracy 0.809), but struggled with specific polymorph identity (0.577). Annealing temperature was the dominant predictor across all targets; oxygen partial pressure showed negligible importance, reflecting narrow literature coverage rather than physical irrelevance. Temperature-dependent XRD on anodized and H2O2-treated Niobium showed structural evolution consistent with the machine learning predictions. Our model and overall approach provide a data-driven framework for identifying and optimizing conditions that promote crystallization in initially amorphous oxides. This framework can guide the selection of growth and post-annealing conditions for Nb surfaces by narrowing the experimental parameter space, thereby reducing trial-and-error efforts in developing oxide structures relevant to SRF applications.
Tilkin, Anthony [Unlisted, US, IL; Fermilab]↗
Peer-to-Peer Communication Trade-Offs for Smart Grid Applications: Preprint
Peer-to-peer energy management systems for smart grids require developers to consider the trade-offs between the amount of communication traffic generated and the quality and speed of convergence of the control algorithms that are deployed. Employing a fully connected communication causes messages to scale exponentially with the number of nodes, while using a sparse connectivity causes less information dissemination leading to degradation of the algorithm performance. The best communication topology for a particular application lies somewhere in between and often requires empirical evaluation by application designers. Existing methods do not put focus on the needs for smart grid applications, which is information dissemination throughout the network and they do not provide a flexible solution for application developers to prototype and deploy different topologies without modifying the application code. This paper introduces a configurable virtual communication topology framework TopLinkMgr, allowing users to specify any chosen communication topology and deploy peer-to-peer applications using it. It also introduces a self-adaptive, fault-tolerant topology management algorithm, Bounded Path Dissemination that can ensure the dissemination of information to all peers within a specified threshold for a sparsely connected topology. Experiments show that the algorithm improves on convergence speed and accuracy over state-of-the-art methods and is also robust against node failures. The results indicate the possibility of achieving a close-to optimal convergence without overloading the network allowing the realization of peer-to-peer control platforms covering larger and more complex power systems.
Bounded Path Dissemination↗
Interference Cognizant Network Scheduling
Systems and methods for interference cognizant network scheduling are provided. In certain embodiments, a method of scheduling communications in a network comprises identifying a bin of a global timeline for scheduling an unscheduled virtual link, wherein a bin is a segment of the timeline; identifying a pre-scheduled virtual link in the bin; and determining if the pre-scheduled and unscheduled virtual links share a port. In certain embodiments, if the unscheduled and pre-scheduled virtual links don't share a port, scheduling transmission of the unscheduled virtual link to overlap with the scheduled transmission of the pre-scheduled virtual link; and if the unscheduled and pre-scheduled virtual links share a port: determining a start time delay for the unscheduled virtual link based on the port; and scheduling transmission of the unscheduled virtual link in the bin based on the start time delay to overlap part of the scheduled transmission of the pre-scheduled virtual link.
Varadarajan, Srivatsan↗
Hybrid Reality Lab Capabilities - Video 2
Our Hybrid Reality and Advanced Operations Lab is developing incredibly realistic and immersive systems that could be used to provide training, support engineering analysis, and augment data collection for various human performance metrics at NASA. To get a better understanding of what Hybrid Reality is, let's go through the two most commonly known types of immersive realities: Virtual Reality, and Augmented Reality. Virtual Reality creates immersive scenes that are completely made up of digital information. This technology has been used to train astronauts at NASA, used during teleoperation of remote assets (arms, rovers, robots, etc.) and other activities. One challenge with Virtual Reality is that if you are using it for real time-applications (like landing an airplane) then the information used to create the virtual scenes can be old (i.e. visualized long after physical objects moved in the scene) and not accurate enough to land the airplane safely. This is where Augmented Reality comes in. Augmented Reality takes real-time environment information (from a camera, or see through window, and places digitally created information into the scene so that it matches with the video/glass information). Augmented Reality enhances real environment information collected with a live sensor or viewport (e.g. camera, window, etc.) with the information-rich visualization provided by Virtual Reality. Hybrid Reality takes Augmented Reality even further, by creating a higher level of immersion where interactivity can take place. Hybrid Reality takes Virtual Reality objects and a trackable, physical representation of those objects, places them in the same coordinate system, and allows people to interact with both objects' representations (virtual and physical) simultaneously. After a short period of adjustment, the individuals begin to interact with all the objects in the scene as if they were real-life objects. The ability to physically touch and interact with digitally created objects that have the same shape, size, location to their physical object counterpart in virtual reality environment can be a game changer when it comes to training, planning, engineering analysis, science, entertainment, etc. Our Project is developing such capabilities for various types of environments. The video outlined with this abstract is a representation of an ISS Hybrid Reality experience. In the video you can see various Hybrid Reality elements that provide immersion beyond just standard Virtual Reality or Augmented Reality.
Delgado, Francisco J.↗
Software defined grid energy storage
Today, consumer battery installations are isolated, physical devices. Virtual power plants (VPPs) allow consumer devices to aggregate for grid services, but they are are vertically integrated, vendor controlled systems (e.g., Tesla’s VPP). Consumer batteries are therefore unable to participate in energy markets or other grid services outside what their vendor provides. We describe a software system that provides software control of multiple, networked battery energy storage systems in the electric grid. The system introduces two new ideas that enable flexible and dependable management of energy storage. The first is a virtual battery, which can either partition a battery or aggregate multiple batteries. The second is a reservation-based API which allows asynchronous control of batteries to meet contractual guarantees in a safe and dependable manner. Virtual batteries and a reservation-based API address the unique challenges of achieving high and efficient utilization of energy storage systems, including heterogeneity of battery systems such as varying C-rates, participation in energy markets, utility bill management systems, community resource sharing, and reliability. Using a testbed comprised of sonnen Inc. storage units installed in several homes and a lab, we demonstrate that virtualized batteries can seamlessly replace physical batteries, flexibly manage energy storage resources, isolate multiple clients using a shared battery, and create new energy storage applications.
25 ENERGY STORAGE↗
Inrush Current Mitigation for Grid-Forming Inverters in Islanded Microgrids
Virtual-inertia and droop control methods are commonly used for grid-forming inverters. While the virtual inertia is used to emulate the equation of motion/frequency, if the inverter output voltage is emulated as in synchronous generators, then the method is known as the virtual synchronous generator. An inductive pulse-load, e.g., a relatively large induction motor, connection to a microgrid fed only by grid-forming inverters may lead to blackout due to high inrush currents. This article presents virtual reactance techniques to mitigate the inrush current effects and enhance the inverter’s robustness for the safe connection of inductive and dynamic loads. This article also compares the virtual inertia and droop control methods under switching inductive-dynamic loads while the proposed techniques are implemented. Experimental tests are performed considering the linear and nonlinear virtual reactance techniques, and the findings are discussed. The mitigation significantly suppresses the inrush currents while the inverters can perform a normal operation. Furthermore, the frequency and power response of the virtual inertia control with different inertia settings to a sudden change in the load is analyzed. The virtual reactance technique is tested in a laboratory-scale hardware setup of a 208V microgrid fed by 5kVA and 10kVA inverters, and the results are presented in this article.
Gursoy, Mehmetcan↗
Biological Visualization, Imaging and Simulation(Bio-VIS) at NASA Ames Research Center: Developing New Software and Technology for Astronaut Training and Biology Research in Space
The Bio- Visualization, Imaging and Simulation (BioVIS) Technology Center at NASA's Ames Research Center is dedicated to developing and applying advanced visualization, computation and simulation technologies to support NASA Space Life Sciences research and the objectives of the Fundamental Biology Program. Research ranges from high resolution 3D cell imaging and structure analysis, virtual environment simulation of fine sensory-motor tasks, computational neuroscience and biophysics to biomedical/clinical applications. Computer simulation research focuses on the development of advanced computational tools for astronaut training and education. Virtual Reality (VR) and Virtual Environment (VE) simulation systems have become important training tools in many fields from flight simulation to, more recently, surgical simulation. The type and quality of training provided by these computer-based tools ranges widely, but the value of real-time VE computer simulation as a method of preparing individuals for real-world tasks is well established. Astronauts routinely use VE systems for various training tasks, including Space Shuttle landings, robot arm manipulations and extravehicular activities (space walks). Currently, there are no VE systems to train astronauts for basic and applied research experiments which are an important part of many missions. The Virtual Glovebox (VGX) is a prototype VE system for real-time physically-based simulation of the Life Sciences Glovebox where astronauts will perform many complex tasks supporting research experiments aboard the International Space Station. The VGX consists of a physical display system utilizing duel LCD projectors and circular polarization to produce a desktop-sized 3D virtual workspace. Physically-based modeling tools (Arachi Inc.) provide real-time collision detection, rigid body dynamics, physical properties and force-based controls for objects. The human-computer interface consists of two magnetic tracking devices (Ascention Inc.) attached to instrumented gloves (Immersion Inc.) which co-locate the user's hands with hand/forearm representations in the virtual workspace. Force-feedback is possible in a work volume defined by a Phantom Desktop device (SensAble inc.). Graphics are written in OpenGL. The system runs on a 2.2 GHz Pentium 4 PC. The prototype VGX provides astronauts and support personnel with a real-time physically-based VE system to simulate basic research tasks both on Earth and in the microgravity of Space. The immersive virtual environment of the VGX also makes it a useful tool for virtual engineering applications including CAD development, procedure design and simulation of human-system systems in a desktop-sized work volume.
Smith, Jeffrey↗
Devices and methods for increasing the speed and efficiency at which a computer is capable of modeling a plurality of random walkers using a particle method
A method for increasing a speed or energy efficiency at which a computer is capable of modeling a plurality of random walkers. The method includes defining a virtual space in which a plurality of virtual random walkers will move among different locations in the virtual space. The method also includes either assigning a corresponding set of ringed neurons in a spiking neural network to a corresponding virtual random walker, or assigning a corresponding set of ringed neurons to a point in the virtual space. Movement of a given virtual random walker is tracked by decoding differences between states of individual neurons in a corresponding given set of ringed neurons. A virtual random walk of the plurality of virtual random walkers is executed using the spiking neural network.
Aimone, James Bradley↗
Combined Mesonet and Tracker
Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]
54 ENVIRONMENTAL SCIENCES↗
Binaural room simulation
In every-day listening the auditory event perceived by a listener is determined not only by the sound signal that a sound emits but also by a variety of environmental parameters. These parameters are the position, orientation and directional characteristics of the sound source, the listener's position and orientation, the geometrical and acoustical properties of surfaces which affect the sound field and the sound propagation properties of the surrounding fluid. A complete set of these parameters can be called an Acoustic Environment. If the auditory event perceived by a listener is manipulated in such a way that the listener is shifted acoustically into a different acoustic environment without moving himself physically, a Virtual Acoustic Environment has been created. Here, we deal with a special technique to set up nearly arbitrary Virtual Acoustic Environments, the Binaural Room Simulation. The purpose of the Binaural Room Simulation is to compute the binaural impulse response related to a virtual acoustic environment taking into account all parameters mentioned above. One possible way to describe a Virtual Acoustic Environment is the concept of the virtual sound sources. Each of the virtual sources emits a certain signal which is correlated but not necessarily identical with the signal emitted by the direct sound source. If source and receiver are non moving, the acoustic environment becomes a linear time-invariant system. Then, the Binaural Impulse Response from the source to a listener' s eardrums contains all relevant auditory information related to the Virtual Acoustic Environment. Listening into the simulated environment can easily be achieved by convolving the Binaural Impulse Response with dry signals and representing the results via headphones.
Lehnert, H.↗
Learning from the Pandemic: the Future of Meetings in HEP and Beyond
The COVID-19 pandemic has by-and-large prevented in-person meetings since March 2020. While the increasing deployment of effective vaccines around the world is a very positive development, the timeline and pathway to "normality" is uncertain and the "new normal" we will settle into is anyone's guess. Particle physics, like many other scientific fields, has more than a year of experience in holding virtual meetings, workshops, and conferences. A great deal of experimentation and innovation to explore how to execute these meetings effectively has occurred. Therefore, it is an appropriate time to take stock of what we as a community learned from running virtual meetings and discuss possible strategies for the future. Continuing to develop effective strategies for meetings with a virtual component is likely to be important for reducing the carbon footprint of our research activities, while also enabling greater diversity and inclusion for participation. This report summarizes a virtual two-day workshop on Virtual Meetings held May 5-6, 2021 which brought together experts from both inside and outside of high-energy physics to share their experiences and practices with organizing and executing virtual workshops, and to develop possible strategies for future meetings as we begin to emerge from the COVID-19 pandemic. This report outlines some of the practices and tools that have worked well which we hope will serve as a valuable resource for future virtual meeting organizers in all scientific fields.
Neubauer, Mark S.↗
Threat emulation framework
A method for emulating threats in virtual network computing environment is provided. The method comprises creating a number of virtual machines in the virtual network computing environment. A number of threat actors are emulated, wherein each threat actor comprises a number of threat artifacts that form a sequence of attack steps against the virtual network computing environment. The threat actors are then deployed against the virtual network computing environment. Behavioral data about actions of the threat actors in the virtual network computing environment is collected, as is performance data about the virtual network computing environment in response to the threat actors. The collected behavioral and performance data is then presented to a user via an interface.
Urias, Vincent↗
Devices and methods for increasing the speed and efficiency at which a computer is capable of modeling a plurality of random walkers using a density method
A method for increasing a speed or energy efficiency at which a computer is capable of modeling a plurality of random walkers. The method includes defining a virtual space in which a plurality of virtual random walkers will move among different locations in the virtual space, wherein the virtual space comprises a plurality of vertices and wherein the different locations are ones of the plurality of vertices. A corresponding set of neurons in a spiking neural network is assigned to a corresponding vertex such that there is a correspondence between sets of neurons and the plurality of vertices, wherein a spiking neural network comprising a plurality of sets of spiking neurons is established. A virtual random walk of the plurality of virtual random walkers is executed using the spiking neural network, wherein executing includes tracking how many virtual random walkers are at each vertex at a given time increment.
Aimone, James Bradley↗